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ABSTRACT
Air pollution is a primary challenging issue in combustion engines which is constantly being addressed. The emission from petrol vehicles such as the carbon monoxide, hydrocarbons, NOX, and so on contribute to about 50 percent of this air pollution. In order to control the pollution caused by petrol engines to the environment, air/fuel mixture plays a very important role. The purpose of this project is to review the studies on the prediction and control of AFR, as well as the different approaches used. These approaches include both modern and classical methods such as the Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy System. AFR of a petrol vehicle is calculated using an emission analyzer to test for its emissions at various RPMs both at idle and loading conditions also while reading its air intake using an onboard diagnostic tool before which the Artificial Neural Networks approach is adopted so as to give air/fuel ratio values with minimum emissions to the environment.
